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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Variational Bayes Logistic Regression with Automatic Relevance Determination"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "VBClassificationARD is modified version of Variational Bayes Logistic Regression with Jaakola&Jordan local bound. Instead of spherical covariance that is used to model prior of coefficients in \n",
      "VB Logistic Regression VBClassificationARD uses diagonal covariance with separate precision parameters for each coefficient."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "What is difference between ClassificationARD and VBClassificationARD?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are two main differences:\n",
      "    - ClassificationARD uses Sparse Bayesian Learner algorithm\n",
      "      to fit model, while VBClassificationARD uses \n",
      "      Variational Bayes\n",
      "\n",
      "    - Both can not compute posterior of coefficients\n",
      "      analytically, so they resort to approximations.     \n",
      "      ClassificationARD uses Laplace approximation, which is \n",
      "      significantly faster but less accurate, while   \n",
      "      VBClassificationARD relies on Jaakola & Jordan \n",
      "      approximation.\n",
      "      \n",
      "In general, it is advised to use VBClassificationARD with small datasets and/or in cases when the accurate approximation of posterior is essential in all other cases ClassificationARD should be preferred."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "VBClassificationARD passes all scikit-learn tests for linear classifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.utils.estimator_checks import check_estimator\n",
      "from skbayes.rvm_ard_models import VBClassificationARD\n",
      "check_estimator(VBClassificationARD)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example:  Classification with many irrelevant features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We use binary classification problem to compare the performance of Regularised Logistic Regression (with L1 and L2 regularizations) with VBClassificationARD. Toy dataset that we use for testing contains 500 features, only two of them are relevant, and the rest is just random noise. The figure below visualizes dataset in space spanned by two relevant features. As you can see two classes are 'almost' linearly separable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def generate_dataset(n_samples = 500, n_features = 100,\n",
      "                     cov_class_1 = [[1.4,.5],[1.,.75]],\n",
      "                     cov_class_2 = [[0.9,0.1],[1.7,.2]],\n",
      "                     mean_class_1 = (1,1),\n",
      "                     mean_class_2 = (-1,-1)):\n",
      "    ''' Generate binary classification problem with two relevant features'''\n",
      "    X   = np.random.randn(n_samples, n_features)\n",
      "    Y   = np.ones(n_samples)\n",
      "    sep = int(n_samples/2)\n",
      "    Y[0:sep]     = 0\n",
      "    X[0:sep,0:2] = np.random.multivariate_normal(mean = mean_class_1, \n",
      "                   cov = cov_class_1, size = sep)\n",
      "    X[sep:n_samples,0:2] = np.random.multivariate_normal(mean = mean_class_2,\n",
      "                        cov = cov_class_2, size = n_samples - sep)\n",
      "    return X,Y\n",
      "\n",
      "X,Y = generate_dataset()\n",
      "plt.figure(figsize = (8,6))\n",
      "plt.plot(X[Y==0,0],X[Y==0,1],\"bo\", markersize = 3)\n",
      "plt.plot(X[Y==1,0],X[Y==1,1],\"ro\", markersize = 3)\n",
      "plt.xlabel('feature 1')\n",
      "plt.ylabel('feature 2')\n",
      "plt.title(\"Example of dataset\")\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8CbiefNHdw8C73P0VzWpooxTgRdIjytSzRveZzxY0PxC3ou0iMxElwB8RYT8r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\n6PS4ctPKVgev94FfsXx5xe+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       "text": [
        "<matplotlib.figure.Figure at 0x1115dfb90>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To compare different algorithms we run 50 simulations in which we randomly generate dataset similar to one in the figure above and classify it using all three algorithms.\n",
      "Obtained results show that VBClassificationARD on average has better accuracy than Logistic Regression with L1 / L2 penalty."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn\n",
      "from matplotlib import cm\n",
      "import matplotlib\n",
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn.linear_model import LogisticRegressionCV\n",
      "\n",
      "lrcverr_l2, lrcverr_l1, vblrerr = [],[],[]\n",
      "n_sim = 50\n",
      "n_samples, n_features = 700, 500\n",
      "\n",
      "\n",
      "for i in range(n_sim): \n",
      "    # Parameters of the example\n",
      "    X,Y = generate_dataset(n_samples,n_features)\n",
      "    \n",
      "    # training & test data\n",
      "    X,x,Y,y = train_test_split(X,Y, test_size = 0.4)\n",
      "    # scale data\n",
      "    mux,stdx = np.mean(X,0),np.std(X,0)\n",
      "    X = (X - mux) / stdx\n",
      "    x = (x - mux) / stdx\n",
      "    \n",
      "    # fit & predict using ARD\n",
      "    vblr = VBClassificationARD()\n",
      "    vblr.fit(X,Y)\n",
      "    y_vblr = vblr.predict(x)\n",
      "        \n",
      "    # fit & predict using cross validated Logistic Regression\n",
      "    # with L1 / L2 penalty\n",
      "    lrcv_l2 = LogisticRegressionCV(penalty = 'l2')\n",
      "    lrcv_l2.fit(X,Y)\n",
      "    y_lrcv_l2 = lrcv_l2.predict(x)\n",
      "    \n",
      "    lrcv_l1 = LogisticRegressionCV(penalty = 'l1', solver = 'liblinear')\n",
      "    lrcv_l1.fit(X,Y)\n",
      "    y_lrcv_l1 = lrcv_l1.predict(x)\n",
      "    \n",
      "    lrcverr_l1.append(np.sum(y!=y_lrcv_l1))\n",
      "    lrcverr_l2.append(np.sum(y!=y_lrcv_l2))\n",
      "    vblrerr.append(np.sum(y!=y_vblr))    \n",
      "\n",
      "    \n",
      "n = x.shape[0]\n",
      "ard = 1 - np.asarray(vblrerr, dtype = np.float)/n\n",
      "l1  = 1 - np.asarray(lrcverr_l1,dtype = np.float)/n\n",
      "l2  = 1 - np.asarray(lrcverr_l2, dtype = np.float)/n \n",
      "better = np.sum((ard > l1) & (ard > l2)) * 100./ n_sim\n",
      "print \"============= Simulation Results =========================== \\n\"\n",
      "print \"Number of simulations = {0} \\n\".format(n_sim)\n",
      "print \"Average accuracy for VBClassificationARD {0} \\n\".format(np.mean(ard))\n",
      "print \"Average accuracy for L2 LogisticRegressionCV {0} \\n\".format(np.mean(l2))\n",
      "print \"Average accuracy for L1 LogisticRegressionCV {0} \\n\".format(np.mean(l1))\n",
      "print (\"VBClassificationARD had better accuracy than L1 / L2 Regularised \"\n",
      "       \"Logistic Regression in {0} percents of simulations\").format(better)\n",
      "print \"=============================================================\\n\"\n",
      "\n",
      "plt.figure(figsize = (8,6))\n",
      "plt.ylim((-.1,1.1))\n",
      "plt.plot(ard,'b-', label = 'VBClassificationARD')\n",
      "plt.plot(l1,'r-', label = 'L1')\n",
      "plt.plot(l2,'g-',label = 'L2')\n",
      "plt.xlabel('simulation index')\n",
      "plt.ylabel('Accuracy')\n",
      "plt.title(\"Accuracy comparison by simulation\")\n",
      "plt.legend(loc = 3)\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "============= Simulation Results =========================== \n",
        "\n",
        "Number of simulations = 50 \n",
        "\n",
        "Average accuracy for VBClassificationARD 0.986 \n",
        "\n",
        "Average accuracy for L2 LogisticRegressionCV 0.8035 \n",
        "\n",
        "Average accuracy for L1 LogisticRegressionCV 0.967714285714 \n",
        "\n",
        "VBClassificationARD had better accuracy than L1 / L2 Regularised Logistic Regression in 98.0 percents of simulations\n",
        "=============================================================\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aNd8WERiob2O3b8dlt8aIiko3GDWNiL47X75cX/xNJ8adOzk/N1O9uh7y7Wpf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\nivlz7v6boFl5jgN33w+8Anwb5TiWrgRGmdlO4FfA1Wb2HMp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       "text": [
        "<matplotlib.figure.Figure at 0x111c1eb50>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example Continues: Predictive Distribution and Decision Boundary"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since we know generating process for each class (i.e. P(X|C1) and P(X|C2)), we can easily compute predictive distribution (i.e. P(C1|X) and P(C2|X)). The figure below compares predictive distribution obtained by each of three models with 'real' predictive distribution obtained through knowledge of the generating process for each class. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Parameters for binary classification problem\n",
      "from scipy.stats import multivariate_normal as mvn\n",
      "n_samples = 500\n",
      "n_features = 500\n",
      "cov1 = [[.9,.1],[.6,.8]]\n",
      "cov2 = [[1.4,.5],[.1,.5]]\n",
      "mean1 = (-0.5,1.5)\n",
      "mean2 = (1,-1.5)\n",
      "np.random.seed(1)\n",
      "\n",
      "X,Y = generate_dataset(n_features = n_features,\n",
      "                       cov_class_1 = cov1,\n",
      "                       cov_class_2 = cov2,\n",
      "                       mean_class_1 = mean1,\n",
      "                       mean_class_2 = mean2)\n",
      "\n",
      "# training & test data\n",
      "X,x,Y,y = train_test_split(X,Y, test_size = 0.4)\n",
      "\n",
      "# fit & predict using ARD\n",
      "vblr = VBClassificationARD()\n",
      "vblr.fit(X,Y)\n",
      "        \n",
      "# fit & predict using cross validated Logistic Regression\n",
      "# with L1 / L2 penalty\n",
      "lrcv_l2 = LogisticRegressionCV(penalty = 'l2')\n",
      "lrcv_l2.fit(X,Y)\n",
      "lrcv_l1 = LogisticRegressionCV(penalty = 'l1', solver = 'liblinear')\n",
      "lrcv_l1.fit(X,Y)\n",
      "    \n",
      "# construct grid    \n",
      "n_grid = 100\n",
      "max_x      = np.max(x[:,0:2],axis = 0)\n",
      "min_x      = np.min(x[:,0:2],axis = 0)\n",
      "X1         = np.linspace(min_x[0],max_x[0],n_grid)\n",
      "X2         = np.linspace(min_x[1],max_x[1],n_grid)\n",
      "x1,x2      = np.meshgrid(X1,X2)\n",
      "Xgrid      = np.zeros([n_grid**2,2])\n",
      "Xgrid[:,0] = np.reshape(x1,(n_grid**2,))\n",
      "Xgrid[:,1] = np.reshape(x2,(n_grid**2,))\n",
      "Xg         = np.random.randn(n_grid**2,n_features)\n",
      "Xg[:,0]    = Xgrid[:,0]\n",
      "Xg[:,1]    = Xgrid[:,1]\n",
      "\n",
      "# estimate probabilities for grid data points\n",
      "vblr_grid    = vblr.predict_proba(Xg)[:,1]\n",
      "lrcv_l2_grid = lrcv_l2.predict_proba(Xg)[:,1]\n",
      "lrcv_l1_grid = lrcv_l1.predict_proba(Xg)[:,1]\n",
      "\n",
      "# compute 'real' predictive distribution\n",
      "def real_posterior(x,mu1,mu2,cov1,cov2,prior1=0.5, prior2=0.5):\n",
      "    lpf1 = mvn.logpdf(x,mu1,cov1) + np.log(prior1)\n",
      "    lpf2 = mvn.logpdf(x,mu2,cov2) + np.log(prior2)\n",
      "    log_prob = np.logaddexp(lpf1,lpf2)\n",
      "    return 1 - np.exp(lpf1 - log_prob)\n",
      "rp = real_posterior(Xg[:,0:2], mean1, mean2, cov1, cov2)\n",
      "\n",
      "# plot data\n",
      "titles = [\"VBClassificationARD\",\"LogisticRegressionCV with L2 penalty\",\n",
      "          \"LogisticRegressionCV with L1 penalty\"]\n",
      "models  = [vblr_grid,lrcv_l2_grid,lrcv_l1_grid]\n",
      "\n",
      "print \"            Model ========================== vs ======================  Real  \"\n",
      "\n",
      "for title,model in zip(titles,models):\n",
      "    fig, axarr = plt.subplots(nrows=1, ncols=2, figsize = (25,10))\n",
      "    axarr[0].contourf(X1,X2,np.reshape(model,(n_grid,n_grid)),cmap=cm.coolwarm)\n",
      "    axarr[1].contourf(X1,X2,np.reshape(rp,(n_grid,n_grid)),cmap=cm.coolwarm)\n",
      "    axarr[0].plot(x[y==0,0],x[y==0,1],\"bo\", markersize = 5)\n",
      "    axarr[1].plot(x[y==0,0],x[y==0,1],\"bo\", markersize = 5)\n",
      "    axarr[0].plot(x[y==1,0],x[y==1,1],\"ro\", markersize = 5)\n",
      "    axarr[1].plot(x[y==1,0],x[y==1,1],\"ro\", markersize = 5)\n",
      "    axarr[0].set_title(title)\n",
      "    axarr[1].set_title(\"Real Predictive Distribution\")\n",
      "    plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "            Model ========================== vs ======================  Real  \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mfa9+3wCAeZA52qlJuXdSTfpe/b7RFm3fQG8UimTYgNlT7dWlzU+a8r36fQMA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\nbU7S9ksAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1117bea50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/6gbIRa+ljLXdvhAwxtgb3nhvp/sAZtksBFxZ9t++Rw+dXvtol2R1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CUKDItiOTYMXOSjCbkkUmvSEmnK2xbWetUTeYkgTJTaD6TaFQiCnSRu8QjLNiKTLdHI2txqyfK0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\nYKz/B4Kr+SXMXwsfAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1131b1f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wGZ1AxveQ8UHmbBbPN+Dp4upjiQJ5KM7/3O7EjM+K5IYwz6kf6gom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       "text": [
        "<matplotlib.figure.Figure at 0x102b00090>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice also how choppy are decision boundaries of both versions of Logistic Regression, that happens because coefficients for irrelevant features are non-zero."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}